Alternatives for Developing Supply Chain Simulation Capabilities

Daniel Dumke — Mon, 13 Jun 2011 14:54:00 +0000

Today I would like to talk about a non-essential, but helpful part of supply chain management: Simulation.

Simulation can be used in a supply chain setting on many different levels. On a strategic level there are models to analyze scenarios for the optimal locations of one’s factory, on a tactical level inventory management and distribution policies are treated and on the operations side route-optimization is a generally used. Of course there are also non-simulation models for these tasks, but this article is not about the pros and cons of that.

The goal of this article is to give you an overview what general differences exist between the approaches to build a non-specialized capability for supply chain simulation.

Approaches for building simulation capabilities

Simulation tools can be categorized into the following scheme.

Data driven simulators, can be defined as a simulation model which can be parameterized by providing data (e.g. by using spreadsheets), it’s purpose is to model a specified set of systems.

Pros: Usually easy to configure for the user

Cons: Less flexibility, since the simulator can only interpret parameters which its programming allow; standard graphical representation, which fits for the set of systems may not be optimal for one special case.

Interactive simulator, builds on modules for supply chain elements. Those have to be arranged and parameterized by the user.

Pros: due to the modularity the visual representation can come very close to the real supply chain

The sub-model approach, is comparable to the module approach, but instead of providing closed modules, models for a sub-system are used.

Pros: The user can better specify the level of detail he needs and also have differing levels of detail for different supply chain tiers.

Cons: Increased requirements for the abilities of the user

Furthermore there are:

Simulator extensions, are extensions written for a general purpose simulation software, which enhance the capabilities to model a specific set of systems.

Object class libraries, are an even lower level where objects are predefined, but not bundled with a simulator, but built on a general programming language.

Comparison and trade-offs

After defining the different approaches the author then compares the different approaches (see figure 1 and 2).

Figure 1: Flexibility / Effort Trade-Off to develop a generic Simulation Capability (Jain, 2008)

Figure 2: Flexibility / Effort Trade-Off to develop a single Model (Jain, 2008)

There is no golden rule of choosing the right approach for building simulation capabilities, but there are some important criteria and trade-offs to select the best approach.

Figure 1 focusses on the development of a generic simulation capability, which could be used to simulate a wider range of problems. If the objective is to develop only one model for a specific purpose (figure 2) the major difference is, that the effort for using sub-models is there higher than using the interactive simulator, since the learning curve for the former is steeper.

Jain names the following criteria, which should be used to decide on the correct approach:

Effort for developing simulation model instance vs. effort for developing a generic simulation capability

Flexibility of the acquired knowledge

Base software to be used

Role of the user in the simulation process

Animation capabilities of the selected product

Utilization of standards (by the simulation software), e.g. in data import / export, model format

Maintenance efforts needed for the capability

Execution time of the simulator

Conclusion

I think Jain could have made a better job to make the difference clear between the decision which software tool to acquire and which capabilities are needed at a company.

These decisions are of course interrelated, but since especially for a larger company it may be also an option to buy capabilities externally or by employing specialists.

For a complete framework the goal orientation was missing as well. For me the first step in such a process would be to define the company’s goals which then lead to the needed capabilities.

But, the reason why I presented this article is that I still think it contains some good classifications to select a fitting approach for creating simulation capabilities.

Reference:

Jain, S. (2008). Tradeoffs in building a Generic Supply Chain Simulation Capability Proceedings of the 2008 Winter Simulation Conference, 1873-1881

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Case Study: Stress Testing Supply Chains

Daniel Dumke — Mon, 07 Feb 2011 15:27:00 +0000

Stress tests are an acknowledged method to test systems under extreme conditions. The method is not only used in engineering (eg. Great picture of a Boing wing stress test (Guardian Eyewitness)), but also in business, most notably and in the banking industry. But can this method also be used to test supply chain designs?

Method / Case Study

There is limited literature on using simulation to evaluate supply chains under stress. The authors took the following approach:

Determining the scope by setting the level of detail and defining the basic elements of the supply chain
Gather data, to improve the reliability the authors worked together with a company which provided the input data for the model. These included the bill of materials, network configuration, etc.
Next the model was implemented. Validity was ensured, by integrating different opinions and thoughts on the model

Results

The company was a supplier for the ministry of defense the major objective therefore was to, make sure that demand can be met during three different scenarios:

Normal operation, with defined volume level
Surge operation, with twice the volume level of normal operation
Mobilization operation, with four times the volume level of normal operation

The simulation showed that all stress scenarios could be finished successfully (each with some weeks of ramp up, of course). Furthermore some configuration changes helped the company to improve performance even more.

Conclusion

In this case stress testing helped the company: to gain a better understanding of their supply chain and to improve supply chain operations. It also helped the customer to gain confidence in the capabilities of its supplier.
I find it an interesting thought, that proven reliability, in this case based on a transparent simulation model, can support a company to gain new customers. But also other companies, which have a strong emphasis on supply chain risk management, are using stress testing for their supply chains, one example would be Dow Chemical.

The complete article can be downloaded here free of charge.

Reference:

Jain, Sanjay, & Leong, Swee (2005). Stress Testing a Supply Chain Using Simulation Proceedings of the 2005 Winter Simulation Conference, 1650-1657

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Sanjay Jain

Alternatives for Developing Supply Chain Simulation Capabilities

Approaches for building simulation capabilities

Comparison and trade-offs

Conclusion

Case Study: Stress Testing Supply Chains

Method / Case Study

Results

Conclusion